{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add the facets overview python code to the python path\n",
    "import sys\n",
    "sys.path.append('./python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load UCI census train and test data into dataframes.\n",
    "import pandas as pd\n",
    "features = [\"Age\", \"Workclass\", \"fnlwgt\", \"Education\", \"Education-Num\", \"Marital Status\",\n",
    "            \"Occupation\", \"Relationship\", \"Race\", \"Sex\", \"Capital Gain\", \"Capital Loss\",\n",
    "            \"Hours per week\", \"Country\", \"Target\"]\n",
    "train_data = pd.read_csv(\n",
    "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
    "    names=features,\n",
    "    sep=r'\\s*,\\s*',\n",
    "    engine='python',\n",
    "    na_values=\"?\")\n",
    "test_data = pd.read_csv(\n",
    "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
    "    names=features,\n",
    "    sep=r'\\s*,\\s*',\n",
    "    skiprows=[0],\n",
    "    engine='python',\n",
    "    na_values=\"?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate the feature statistics proto from the datasets and stringify it for use in facets overview\n",
    "from generic_feature_statistics_generator import GenericFeatureStatisticsGenerator\n",
    "import base64\n",
    "\n",
    "gfsg = GenericFeatureStatisticsGenerator()\n",
    "proto = gfsg.ProtoFromDataFrames([{'name': 'train', 'table': train_data},\n",
    "                                  {'name': 'test', 'table': test_data}])\n",
    "protostr = base64.b64encode(proto.SerializeToString()).decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<link rel=\"import\" href=\"/nbextensions/facets-dist/facets-jupyter.html\" >\n",
       "        <facets-overview id=\"elem\"></facets-overview>\n",
       "        <script>\n",
       "          document.querySelector(\"#elem\").protoInput = 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       "        </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the facets overview visualization for this data\n",
    "from IPython.core.display import display, HTML\n",
    "\n",
    "HTML_TEMPLATE = \"\"\"<link rel=\"import\" href=\"/nbextensions/facets-dist/facets-jupyter.html\" >\n",
    "        <facets-overview id=\"elem\"></facets-overview>\n",
    "        <script>\n",
    "          document.querySelector(\"#elem\").protoInput = \"{protostr}\";\n",
    "        </script>\"\"\"\n",
    "html = HTML_TEMPLATE.format(protostr=protostr)\n",
    "display(HTML(html))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
